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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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Editorial

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2024年第3期6卷 289-289页

作者： Liao, Shengcai Liu, Xiaoming Tistarelli, Massimo Chellappa, Rama Kittler, Josef United Arab Emirates University College of Information Technology Al Ain United Arab Emirates Michigan State University Department of Computer Science and Engineering East LansingMI48824 United States University of Sassari Computer Vision Laboratory Sassari07100 Italy Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD21218 United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD21218 United States University of Surrey Centre for Vision Speech and Signal Processing GuildfordGU2 7XH United Kingdom

The 2022 International Joint Conference on Biometrics (IJCB 2022) was held at Abu Dhabi, United Arab Emirates, from October 10 to October 13, 2022. This marked the first biometrics conference held in the Middle East. The IJCB 2022 conference combined two major biometrics conferences: the IEEE Biometrics Theory, Applications, and Systems (BTAS) conference and the International Conference on Biometrics (ICB). The IJCB 2022 conference was technically co-sponsored by both IAPR TC4 (Technical Committee on Biometrics) and the IEEE Biometrics Council. © 2019 IEEE.

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Graph-based Simultaneous Localization and Bias Tracking

arXiv

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arXiv 2023年

作者： Venus, Alexander Leitinger, Erik Tertinek, Stefan Meyer, Florian Witrisal, Klaus Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Christian Doppler Laboratory for Location-aware Electronic Systems Austria NXP Semiconductors Gratkorn Austria Department of Electrical and Computer Engineering and Scripps Institution of Oceanography University of California San Diego San DiegoCA United States

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent’s position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks. © 2023, CC BY.

关键词： Geometry

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Federated multimedia Recommendation Systems with Privacy Protection

Federated Multimedia Recommendation Systems with Privacy Pro...

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IEEE International Symposium on Broadband multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Scott Chih-Hao Huang Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China Department of Electrical Engineering National Tsinghua University Hsinchu Taiwan

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In this big data era, distributed machine learning and global data pooling have become quite critical in practical implementation of any multimedia data-science technique. The emerging federated multimedia recommendation systems (FeMRSs) offer a promising approach to address the need of personalized recommendations subject to both users’ privacy and data security. However, there exists no quantitative method for assessing the discrepancy between the large original rating matrix stored on the server and the dimensionality-reduced rating matrix produced on each client’s equipment. Meanwhile, there lacks a systematic means to evaluate the differential privacy (DP), which is a critical security measure in many distributed systems, particularly when sensitive data are processed. In this work, we first introduce a novel personalized dimensionalityreduction algorithm utilizing the matrix sketching technique. This new algorithm effectively control the difference between the original rating matrix on the server and the dimensionality-reduced rating matrix on each client’s equipment. Moreover, a randomized DP matrix factorization algorithm is designed to be executed at each client’s equipment. The theoretical proof is also carried out to show how much DP can be attained by use of the aforementioned randomized DP matrix factorization algorithm. Finally, extensive numerical studies are presented to evaluate the effectiveness of our proposed novel algorithms using both simulated and real datasets for building the FeMRSs.

关键词： dimensionality reduction Digital multimedia broadcasting Privacy Systematics Distributed databases Filtering algorithms Servers

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Novel Personalized multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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2023 IEEE International Symposium on Broadband multimedia Systems and Broadcasting, BMSB 2023

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China Guilin University of Electronic Technology Department of Information and Telecommunication Guilin541004 China Northwestern State University of Louisiana Department of Engineering Technology NatchitochesLA71497 United States Communications Research Centre Canada OttawaONK2H 8S2 Canada

ISBN: (纸本)9798350321524

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users' preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user's on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems. © 2023 IEEE.

关键词： Recommender systems

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Graph-based Simultaneous Localization and Bias Tracking for Robust Positioning in Obstructed LOS Situations

Graph-based Simultaneous Localization and Bias Tracking for ...

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Asilomar Conference on signals, Systems & computers

作者： Alexander Venus Erik Leitinger Stefan Tertinek Florian Meyer Klaus Witrisal Signal Processing and Speech Communication Laboratory Graz University of Technology Christian Doppler Laboratory for Location-aware Electronic Systems NXP Semiconductors Austria Department of Electrical and Computer Engineering Scripps Institution of Oceanography University of California San Diego

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent's position together with a time-varying number of multipath components (MPCs). The MPCs are represented by “delay biases” corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to correctly infer the agent position even in fully obstructed LOS situations. In a challenging scenario we demonstrate the presented algorithm to significantly outperform state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm attains the posterior Cramér-Rao lower bound (PCRLB).

关键词： Location awareness Tracking Atmospheric measurements Mobile agents Clustering algorithms Position measurement Particle measurements

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Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum processing Units

Demonstrating the Potential of Adaptive LMS Filtering on FPG...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Hans Johnson Nicholas Bornman Taeyoon Kim David Van Zanten Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Fermi National Accelerator Laboratory Superconducting Quantum Materials and Systems (SQMS) Center Batavia IL U.S.A. Department of Physics and Astronomy Center for Applied Physics and Superconducting Technologies (CAPST) Northwestern University Evanston IL U.S.A.

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.

关键词： Three-dimensional displays Accuracy Filtering Qubit signal processing algorithms Superconducting filters Real-time systems Reliability Field programmable gate arrays signal to noise ratio

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Effects of spatially dense adrenergic stimulation to rotor behaviour in simulated atrial sheets

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computers in Biology and Medicine 2024年 182卷 109195-109195页

作者： Magtibay, Karl Massé, Stéphane Nanthakumar, Kumaraswamy Umapathy, Karthikeyan Biomedical Signal and Image Processing Laboratory Department of Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Metropolitan University 350 Victoria St TorontoONM5B 2K3 Canada Toby Hull Cardiac Fibrillation Management Toronto General Hospital University Health Network 200 Elizabeth Street TorontoONM5G 2C4 Canada

Sympathetic hyperactivity via spatially dense adrenergic stimulation may create pro-arrhythmic substrates even without structural remodelling. However, the effect of sympathetic hyperactivity on arrhythmic activity, such as rotors, is unknown. Using simulations, we examined the effects of gradually increasing the spatial density of adrenergic stimulation (AS) in atrial sheets on rotors. We compared their characteristics against rotors hosted in atrial sheets with increasing spatial density of minimally conductive (MC) elements to simulate structural remodelling due to injury or disease. We generated rotors using an S1-S2 stimulation protocol. Then, we created phase maps to identify phase singularities and map their trajectory over time. We measured each rotor's duration (s), angular speed (rad/s), and spatiotemporal organization. We demonstrated that atrial sheets with increased AS spatial densities could maintain rotors longer than with MC elements (2.6 ± 0.1 s vs. 1.5 ± 0.2 s, p © 2024 The Authors

关键词： Digital elevation model

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Monaural Speech Enhancement Using a multi-Branch Temporal Convolutional Network

SSRN

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SSRN 2022年

作者： Zhang, Qiquan Qian, Xinyuan Nicolson, Aaron Wang, Chenxu Paliwal, Kuldip K. Department of Electrical and Computer Engineering National University of Singapore Singapore Australian e-Health Research Centre CSIRO Australia School of Information and Electrical Engineering Harbin Institute of Technology Weihai264209 China Signal Processing Laboratory Griffith University BrisbaneQLD4160 Australia

Deep learning has achieved substantial improvement on single-channel speech enhancement. The performance of multi-layer perceptrons (MLPs)-based methods is limited by the ability to capture the long-term contextual information. The recurrent neural networks (RNNs), e.g., long short-term memory (LSTM) model, are able to capture the long-term temporal dependencies, but suffer from the high latency and the complex training process. Recently, temporal convolution networks (TCNs), employing 1-D dilated convolution and residual learning, achieves gained considerable success in speech enhancement. In this paper we propose a novel TCN model that employs multi-branch structure, called multi-branch TCN (MB-TCN), for monaural speech enhancement. The MB-TCN exploits split-transform-aggregate design, which is expected to obtain strong representational power with higher parameter efficiency. The MB-TCN model applies 1-D causal dilated CNN in each branch to expand receptive fields for capturing long-term temporal contextual information. Our extensive experimental investigation suggests that MB-TCNs outperform the residual LSTMs (ResLSTMs), TCNs, and the CNNs with dense aggregations (DenseNet) in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our proposed MB-TCN model demonstrates highly competitive performance to multiple state-of-the-art speech enhancement methods in terms of five widely used objective metrics. © 2022, The Authors. All rights reserved.

关键词： Convolution

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